{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-2-a382ba2c735b>:15: MatplotlibDeprecationWarning: Passing the pad parameter of tight_layout() positionally is deprecated since Matplotlib 3.3; the parameter will become keyword-only two minor releases later.\n",
      "  plt.tight_layout(.5)\n",
      "findfont: Font family ['serif'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams.update({\n",
    "    \"font.family\": \"serif\",\n",
    "    \"font.serif\": [],                    # use latex default serif font\n",
    "    \"font.sans-serif\": [\"DejaVu Sans\"],  # use a specific sans-serif font\n",
    "})\n",
    "\n",
    "plt.figure(figsize=(4.5, 2.5))\n",
    "plt.plot(range(5))\n",
    "plt.text(0.5, 3., \"serif\")\n",
    "plt.text(0.5, 2., \"monospace\", family=\"monospace\")\n",
    "plt.text(2.5, 2., \"sans-serif\", family=\"sans-serif\")\n",
    "plt.text(2.5, 1., \"comic sans\", family=\"Comic Sans MS\")\n",
    "plt.xlabel(\"µ is not $\\\\mu$\")\n",
    "plt.tight_layout(.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
